import gradio as gr
from src.transcriber import transcriber

def main():

    audio_chunked = gr.Interface(
            fn=transcribe_chunked_audio,
            inputs=[
                gr.Audio(sources=["upload"], label="Audio file", type="filepath"),
                gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
                gr.Checkbox(value=False, label="Return timestamps"),
            ],
            outputs=[
                gr.Textbox(label="Transcription", show_copy_button=True),
                gr.Textbox(label="Transcription Time (s)"),
            ],
            allow_flagging="never",
            title=title,
            description=description,
            article=article,
        )
    
    with gr.Blocks(title='multilang-asr-transcriber', delete_cache=(86400, 86400), theme=gr.themes.Base()) as demo:
        gr.Markdown('## Multilang ASR Transcriber')
        gr.Markdown('An automatic speech recognition tool using [faster-whisper](https://github.com/SYSTRAN/faster-whisper). Supports multilingual video transcription and translation to english. Users may set the max words per line.')
        file = gr.File(file_types=["video", "audio"],type="filepath", label="Upload video or audio")
        max_words_per_line = gr.Number(value=6, label="Max words per line")
        task = gr.Radio(choices=["transcribe", "translate"], value="transcribe", label="Select Task")
        model_version = gr.Radio(choices=["deepdml/faster-whisper-large-v3-turbo-ct2", "large-v3"], value="deepdml/faster-whisper-large-v3-turbo-ct2", label="Select Model")
        text_output = gr.Textbox(label="SRT Text transcription", show_copy_button=True)
        srt_file = gr.File(file_count="single", type="filepath", file_types=[".srt"], label="SRT file")
        text_clean_output = gr.Textbox(label="Text transcription", show_copy_button=True)
        gr.Interface(transcriber,
                    inputs=[file, max_words_per_line, task, model_version],
                    outputs=[text_output, srt_file, text_clean_output],
                    allow_flagging="never")
    demo.launch()

if __name__ == '__main__':
    main()